Impact on outcomes of measuring lactates prior to ICU in unselected heterogeneous critically ill patients: A propensity score analysis

Background Elevated blood lactate levels were reported as effective predictors of clinical outcome and mortality in ICU. However, there have been no studies simply comparing the timing of measuring lactates before vs. after ICU admission. Methods A total of 19,226 patients with transfer time ≤ 24 hr were extracted from the Medical Information Mart for Intensive Care IV database (MIMIC-IV). After 1:1 propensity score matching, the patients were divided into two groups: measuring lactates within 3 hr before (BICU group, n = 4,755) and measuring lactate within 3 hr after ICU admission(AICU group, n = 4,755). The primary and secondary outcomes were hospital mortality, hospital 28-day mortality, ICU mortality, ICU length of stay (LOS), hospital LOS, and restricted mean survival time (RMST). Results Hospital, hospital 28-day, and ICU mortality were significantly higher in AICU group (7.0% vs.9.8%, 6.7% vs. 9.4%, and 4.6% vs.6.7%, respectively, p<0.001 for all) Hospital LOS and ICU LOS were significantly longer in AICU group (8.4 days vs. 9.0 days and 3.0 days vs. 3.5 days, respectively, p<0.001 for both). After adjustment for predefined covariates, a significant association between the timing of measuring lactate and hospital mortality was observed in inverse probability treatment weight (IPTW) multivariate regression, doubly robust multivariate regression, and multivariate regression models (OR, 0.96 [95%CI, 0.95-0.97], OR 0.52 [95%CI, 0.46-0.60], OR 0.66 [95%CI, 0.56-0.78], respectively, p<0.001 for all), indicating the timing as a significant risk-adjusted factor for lower hospital mortality. The difference (BICU-AICU) of RMST at 28- days after ICU admission was 0.531 days (95%CI, 0.002-1.059, p<0.05). Placement of A-line and PA-catheter, administration of intravenous antibiotics, and bolus fluid infusion during the first 24-hr in ICU were significantly more frequent and faster in the BICU vs AICU group (67.6% vs. 51.3% and 126min vs.197min for A-line, 19.6% vs.13.2% and 182min vs. 274min for PA-catheter, 77.5% vs.67.6% and 109min vs.168min for antibiotics, and 57.6% vs.51.6% and 224min vs.278min for bolus fluid infusion, respectively, p<0.001 for all). Additionally, a significant indirect effect was observed in frequency (0.19879 [95% CI, 0.14061-0.25697] p<0.001) and time (0.07714 [95% CI, 0.22600-0.13168], p<0.01) of A-line replacement, frequency of placement of PA-catheter (0.05614 [95% CI, 0.04088-0.07140], p<0.001) and frequency of bolus fluid infusion (0.02193 [95%CI, 0.00303-0.04083], p<0.05). Conclusions Measuring lactates within 3 hr prior to ICU might be associated with lower hospital mortality in unselected heterogeneous critically ill patients with transfer time to ICU ≤ 24hr, presumably due to more frequent and faster therapeutic interventions.


Introduction
An elevated blood lactate level mainly results from anaerobic metabolism caused by tissue hypoxia, accelerated aerobic glycolysis via the Na-K ATPase due to excess β-adrenergic stimulation, or impaired clearance from the liver [1,2]. While blood lactate level is not a direct reflection of tissue perfusion, it can serve as a surrogate marker of poor tissue perfusion [3].
It is conceivable that elevated blood lactate levels prior to ICU admission may hasten therapeutic interventions, and thereby would improve morbidity or mortality. However, a large number of studies appear to discuss the lactate values alone without considerations of lactate data prior to ICU admission. To our knowledge, there have been no comparative studies of the timing of measuring lactates before and after ICU admission.
Thus, the present study was conducted to simply compare the impacts of measuring lactates within 3 hr before vs. within 3 hr after ICU admission on outcomes in unselected heterogeneous ICU patients.

Data sources
The medical information mart for intensive care (Multiparameter Intelligent Monitoring for Intensive Care IV [MIMIC-IV] version 0.4) is a large, freely available database comprising deidentified health-related data associated with 53,150 patients who stayed in seven ICUs of the Beth Israel Deaconess Medical Center (Boston, MA, USA) between 2008 and 2019 [19,20]. The use of the MIMIC-IV database was approved for HE after certification of the CITI program by the Massachusetts Institute of Technology (No. 25459972).

Patient selection
Eligibility criteria included the first ICU admission on a same hospital admission, ICU length of stay (LOS) � 12hr, age �18 years, and the time interval between hospital admission and ICU admission (transfer time) � 24 hr.
According to the timing of measuring lactates relevant to ICU admission, the patients were divided into two groups: measuring lactates within 3 hr before (BICU group) vs. measuring lactates within 3 hr after ICU admission (AICU group).

Variable extraction
The The structured query language (SQL) scripts for data extraction are available on the GitHub website (https://github.com/MIT-LCP/mimic-IV).

Study endpoints
The primary exposure was the timing of measuring lactates relevant to ICU admission (BICU group vs. AICU group). The primary outcome was to examine the difference of hospital mortality between the two groups.
The secondary outcome included differences in ICU mortality, hospital 28-day mortality, hospital LOS, ICU LOS, and restricted mean survival time [21,22] between the two groups.

Statistical analysis
Descriptive statics were computed for all variables, and normal distribution was assessed using Shapiro-Wilk test. Categorical variables were presented as numbers and percentages (%) and were compared using chi-squared test. Continuous variables were presented as mean±standard deviation (SD) for variables with normal distribution or as median (interquartile range [IQR]) for variables without normal distribution and compared using either Student's t-test or Mann-Whitney U test, respectively.
A propensity score (PS), probability that a patient would have been treated (BICU group), based on all baseline variables, was calculated for each patient [23]. Then, the inverse probability of treatment weighting (IPTW) was calculated as 1/PS for patients in the BICU group, and as 1/(1-PS) for patients in the AICU group [23]. Finally, PS matching (PSM) was conducted to control potential confounding factors and to obtain a balanced retrospective cohort. 1:1 matching using the nearest-neighbor method within a caliper width equal to 0.1 of the SD of the PS was implemented. Standardized mean difference (SMD) was calculated before and after PSM to assess the 'balance'.
The association between the timing of lactate measures (BICU vs. AICU group) and hospital mortality was analyzed by multivariate logistic regression model for the original cohort (before PSM), IPTW regression model for the original cohort, doubly robust model for the original cohort, and multivariate logistic regression model for the PSM cohort (after PSM). All models were adjusted for covariates that were considered as clinically relevant baseline variables (age, gender, ethnicity, ICU type, admission type, CCI, ICU severity scores, and initial lactate level). The results of the regression models were presented as odds ratio (OR) and 95% confidence intervals (CI).
To clarify mediators affecting the association between the timing of lactate measures (exposure) and hospital mortality (outcome), causal mediation analysis (CMA) was implemented for the PSM cohort. The CMA decomposes the effect of timing of lactate measure on hospital mortality (total effect) into direct effect and indirect effect mediated via mediator [24]. In the present study, therapeutic interventions during the first 24hr after ICU admission were selected as mediator variables.
Additionally, restricted mean survival time (RMST) [21,22] and mean difference of RMSTs between BICU and AICU group were calculated for specific time points (τ): 28-days and 60days after ICU admission.
Two-sided p values <0.05 were considered significant. All analyses were performed using software Stata/SE package version 16.0 (StataCorp, College Station, TX, USA) or the free software package "R" version 4.2.1.

Baseline characteristics
From the MIMIC-IV database, consisting of 76,540 ICU admissions, 53,150 patients, and 69,211 hospital admission, a total of 19,226 patients were finally included as the original cohort, and divided into BICU group (n = 6,978) and AICU group (n = 12,248) (Fig 1). After 1:1 PSM, 4,755 patients remained in each of the BICU or AICU groups, as the PSM cohort (Fig 1). Comparisons of baseline variables between the two groups in both original and PSM cohorts are summarized in Table 1.
In the PSM cohort, SMD for all baseline variables was � 0.051, indicating similar distribution, and there were no significant differences in variables between the two groups.
Comparisons of baseline variables between survivors and non-survivors in total, BICU, and AICU groups in the PSM cohort are summarized in S2 Table.

Primary outcome
In both original (n = 19,226) and PSM (n = 9,510) cohorts, hospital mortality rate differed significantly between the two groups (5.4% for BICU and 16.0% for AICU group in the original cohort, and 7.0% for BICU and 9.8% for AICU group in the PSM cohort, p<0.001 for both) ( Table 2).
The results of the 4 multivariate regression models for hospital mortality after adjustment for the predefined covariates are shown in Table 3.
There was a significant OR for hospital mortality in all 4 models (range of ORs: 0.50-0.96, p<0.001 for all). Thus, lactate measures within 3 hr prior to ICU admission was suggested as a risk-adjusted factor for lower hospital mortality.

Secondary outcomes
Results of secondary outcomes in both cohorts are shown in Table 2.
In the PSM cohort, both hospital 28-day mortality rate and ICU mortality rate differed significantly between the two groups (6.7% for BICU vs.9.4% for AICU group, and 4.6% for BICU vs. 6.7% for AICU group, respectively, p<0.001 for both). Hospital LOS and ICU LOS were also different between the two groups (8.4 days for BICU vs. 9.0 days for AICU group, and 3.0 days for BICU vs. 3.5 days for AICU group, respectively, p<0.001 for both). RMST at 28-days after ICU admission was significantly different between the two groups (24.7 days for BICU vs. 24 (Fig 2).
In the original cohort, there were highly statistically significant differences in hospital mortality, hospital 28-day mortality, ICU mortality, hospital LOS, ICU LOS, and both the RMSTs at 25-days and 60-days between the two groups (p<0.001 for all) (S1 Fig).

Subgroup analysis
Two subgroup analyses were implemented in the PSM cohort and results are shown in Figs 3 and 4, respectively.
Results of the subgroup analysis of initial lactate level, first-day Sepsis-3, transfer time, age, and gender are shown in

Interventions within the first 24hr after admission to ICU
Comparisons of the first 24 hr therapeutic interventions between the two groups in the PSM cohort are summarized in Table 4.
There was a significant difference in total number of lactate measures during the first 3 hr (6,986 for BICU vs. 6,167 for AICU group, p<0.001) and during the first 6 hr (10,359 for BICU vs.8,406 for AICU group, p<0.001).
However, there were no significant differences in the use of mechanical ventilation, use of vasopressors, use of RRT, and placement of CV-line between the two groups. To the contrary, minutes to vasopressors was significantly shorter in the AICU group (173 min vs. 66 min, p<0.001).

Causal mediation analysis
The CMA allows decomposing the total effect of an exposure on an outcome into a direct effect of the exposure on the outcome and an indirect effect that acts through a mediator of interest by using the counterfactual approach [7,11,24]. Several methods and software programs have been developed for CMA [25]. We adopted the difference method based on fitting two parametric regression models [26]. The results of the CMA for therapeutic intervention are summarized in S3 Table. There  Thus, the beneficial effects of lactate measures before ICU admission are suggested to be partly mediated through the three therapeutic interventions.

Discussion
We performed a simple comparative study of measuring lactates before vs. after ICU admission in unselected heterogeneous ICU patients extracted from the MIMIC-IV database. Consequently, in comparison with measures within 3 hr after ICU admission, lactate measures within 3 hr prior to ICU admission were significantly associated with lower risk-adjusted hospital mortality in 4 different multivariate regression models (Table 3).
In particular, the IPTW model generates a new weighted score for treatment and control on individual patients, preventing the loss of PS unmatched patients in the PSM cohort, and mimics a randomized controlled trial by avoiding selection bias [23,27]. Additionally, the doubly robust model requires one model for the outcome and another model for the exposure but is consistent if either model is correct, not necessary both, providing double chances to make a valid inference [27,28]. Both IPTW and doubly robust models are generally accepted to efficiently evaluate average treatment effect (ATE) [27,28].

Timing of measuring lactates
It seems rational that the latest lactate data prior to ICU may trigger prompt responses and enable rapid therapeutic interventions. For the septic patients, the surviving sepsis campaign recommends "Hour-1 Bundle": when initial lactate level > 2 mmol/L, it should be remeasured within 2-4 hr to guide resuscitation to normalize lactate in patients with elevated lactate levels as a marker of tissue hypoperfusion [29]. Chen et al. [7] have clearly documented that early lactate measurement within 1hr after ICU admission was significantly associated with lower 28-day mortality, probably, due to a shorter time to vasopressors in septic patients with an initial lactate level >2.0 mmol/L extracted from the MIMIC-III database, and that the adequate time interval between early and the second measurement was within 3 hr. Similarly, Chen et al. [11] have shown that early lactate measurement within 1 hr after ICU admission was significantly associated with lower 28-day mortality, mediated through shorting the time to initial IV fluid, in adult patients with hypotension and hyperlactatemia extracted the MIMIC-III and eICU Collaborative Research Database. Unfortunately, no lactate data prior to ICU admission were shown in either of those studies.
In contrast, our study investigated the association of the timing of measuring lactate relevant to ICU admission and hospital mortality in unselected heterogeneous critically ill patients with normal to elevated blood lactate levels.

Subgroup analysis
In the subgroup analysis of transfer time, the patients with delayed transfer time to ICU (> 12 hr) did not show a significant OR for hospital mortality (Fig 3). Several other studies [30][31][32] have indicated that delayed transfer time to ICU was associated with poor outcomes. Churpek et al. [31] described a significant association of delayed transfer time (� 6hr) and increased hospital mortality in 3,789 medical-surgical ward patients. Similarly, Chalfin et al. [32] described that ED critically ill patients with � 6hr delay to ICU (n = 1,036) had increased hospital LOS, higher rate to admit higher level of ICU, and higher hospital mortality in 50,322 ICU patients. Considering the transfer time from hospital admission to ICU admission in this study, not from ward or ED, it appears to be comparable to time delay of 6 hr in the Chalfin or Chalfin study.
In the subgroup analysis of 3 lactate levels, even the patients with normal lactate levels � 1.5mmol/L showed a significant OR for hospital mortality (Fig 3). A normal lactate level in unstressed individuals is 1.0±0.5 mmol/L [33]. However, Nicol et al. [33] documented significantly higher ORs for hospital mortality at lactate levels of 0.75-1.0,1.01-1.25, and 1.26-1.50 mmol/L on ICU admission, compared to OR of 1.0 at lactate levels of 0-0.75 mmol/L in 7,155 ICU patients. Additionally, Chebl et al. [17] documented that critically ill patients with normal lactate levels on ICU admission (49.6%) had a high hospital mortality rate of 19.6% and ICU mortality rate of 9.8% in 450 ED patients.

Restricted mean survival time (RMST)
RMST estimates are the truncated averaged area from the start of follow-up to a predefined follow-up time point (τ) under the Kaplan-Meier survival curve [21,22]. In other words, RMST means the τ-specific life expectancy [22]. The specific points (τ) of 28-days and 60-days in this study were chosen based on a study by Zhou et al. [8], who evaluated the timing of albumin administration in septic patients extracted from the MIMIC-IV database.
RMST difference between treatment and control groups means an interpretable and intuitive expression of ATE, but with a relatively small effect size [34].

Limitations
The present study has several limitations. First, this retrospective study was based on 7 ICUs at a single institutional database. Thus, the treatments against elevated lactate levels were not protocoled or uniformly reported due to the retrospective nature of the study and unselected heterogeneous cohort. Second, there were no statistically significant differences between the two groups in categorical data (ethnicity, admission type, and ICU type) after PSM. However, the individual classification for the categorical data was arbitrarily assigned, not based on explicit reasons, may leading to selection bias. Third, the measured lactate in both groups was not always the first measure after hospital admission. Therefore, therapeutic interventions after the first measure were not considered. Finally, unpredictable confounders and selection biases might exist, affecting the present findings. Thus, further prospective randomized trials are needed.

Conclusions
In unselected heterogeneous ICU patients with transfer time to ICU admission (� 24hr), measuring lactate within 3 hr prior to ICU might be associated with lower hospital mortality presumably due to more frequent and faster therapeutic interventions.